Aquí os dejo con un ejemplo (red_neuronal_clasica.py) muy sencillo y claro para realizar una red neuronal clásica.
import theano from theano import tensor as T import numpy as np from Load import mnist from scipy.misc import imsave def floatX(X): return np.asarray(X, dtype=theano.config.floatX) def init_weights(shape): return theano.shared(floatX(np.random.randn(*shape) * 0.01)) def sgd(cost, params, lr=0.05): grads = T.grad(cost=cost, wrt=params) updates = [] for p, g in zip(params, grads): updates.append([p, p - g * lr]) return updates def model(X, w_h, w_o): h = T.nnet.sigmoid(T.dot(X, w_h)) pyx = T.nnet.softmax(T.dot(h, w_o)) return pyx trX, teX, trY, teY = mnist(onehot=True) X = T.fmatrix() Y = T.fmatrix() w_h = init_weights((784, 625)) w_o = init_weights((625, 10)) py_x = model(X, w_h, w_o) y_x = T.argmax(py_x, axis=1) cost = T.mean(T.nnet.categorical_crossentropy(py_x, Y)) params = [w_h, w_o] updates = sgd(cost, params) train = theano.function(inputs=[X, Y], outputs=cost, updates=updates, allow_input_downcast=True) predict = theano.function(inputs=[X], outputs=y_x, allow_input_downcast=True) for i in range(100): for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 128)): cost = train(trX[start:end], trY[start:end]) print(np.mean(np.argmax(teY, axis=1) == predict(teX)))
